79 citations · 164 across the 7 of their papers we have counts for
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cs.LG2022
Improving Your Graph Neural Networks: A High-Frequency Booster
Jiaqi Sun, Lin Zhang, Shenglin Zhao +1
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node cla…
cs.LG2020★ 1 cited
Effective Data-aware Covariance Estimator from Compressed Data
Xixian Chen, Haiqin Yang, Shenglin Zhao +2
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted…
cs.LG2020★ 7 cited
Making Online Sketching Hashing Even Faster
Xixian Chen, Haiqin Yang, Shenglin Zhao +2
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However…